1.1 About this document

This document describes the specifications for the OMs, CMPs, and PMs that have been proposed and developed for the North Atlantic swordfish (hereafter swordfish) fishery.

It is a living document and will be continued to be updated so that it reflects the current state of the swordfish MSE process.

Members of the Swordfish Species Group (hereafter the Group) are encouraged to provide feedback, comments, or edits to any part of this document.

The document is written using the Markdown format and can edited in any text editor. The source document is available on the ICCAT/nswo-mse GitHub repository.

Group members can make edits to the document either directly in the online repository or by cloning the repository and submitting pull requests with their edits. Alternatively, they can email questions or comments to Adrian. The former approach has the advantage that all comments, questions, and edits are immediately visible to all members of the Group.

Group members can also use the Discussions feature on the Github repository to post questions, comments, or points for discussion related to any aspect of this document or the MSE process in general.

This document is available at the North Atlantic Swordfish MSE homepage.

3.1 Data

The assessment used landings data from 8 longline fleets (7 national fleets and ‘Other’ representing all catches not accounted for in the national fleets) (Table 3.1 and Figure 3.1).

There were 6 indices of abundance derived from catch-per-unit-effort (CPUE) data from the national fleets (Table 3.1 and Figure 3.1). Data from the Japan longline fleet was split into two phases: Early (1950 - 1993), and Late (1994 - 2020) due to changes in fishing practices in these periods. Similarly, the Chinese-Taipei longline fleet was split into two phases: Early (1968 - 1989) and Late (1997 - 2020). Five additional survey indices were developed from the age-specific (ages estimated from length data) CPUE from the Spain longline fleet (Table 3.1 and Figure 3.1).

Length composition data from 7 national longline fleets (Spain, U.S., Canada, Japan (Early, Late), Portugal, Chinese-Tapai (Early, Late), and Morocco) and the Canada/USA harpoon fleet (Figure 3.1). Mean weight data from two fleets (U.S. and Canada) were also used (Figure 3.1).

The catchability coefficient (q) for the CPUE indices for Canada, Morocco, and the Age-1, Age-2, and Age-4 Spain age-specific survey indices, was made a function of the Atlantic Multidecadal Oscillation (AMO) (see SCRS/2022/124 for details).

The effective sample size (ESS) for the length composition data was established by adjusting ESS until unity was reached between modeled ESS and the Francis suggested sample size (See Anon., 2017 for details).

Figure 3.1: Summary of the data used in the 2022 stock assessment.

3.2 Model Structure

The SS3 model used one season, one area, and two sexes.

3.3 Fixed Biological Parameters

Natural mortality for both male and female was fixed at 0.2 for all age classes. Maturity-at-age was knife-edge, with 50% at age-5 and 100% thereafter. Fecundity was proportional to body weight. The growth parameters for the 2022 assessment were fixed at same used in the 2017 assessment, which were developed during the 2017 ICCAT Swordfish Data Preparatory Meeting (See Anon., 2017 for details).

3.4 Selectivity

Selectivity was modeled as a function of length. Dome-shaped selectivity was allowed for five fleets: EU-Spain, USA, Japan, EU-Portugal, and Morocco. Asymptotic selectivity was assumed for Canada, Chinese-Taipei and Other. The age-specific survey CPUEs were modeled with a fixed age-based selectivity.

3.5 Retention and Discard Mortality

The 2022 assessment assumed a minimum legal length of 119 cm lower jaw fork length (LFFL) for all fleets from 1993 - 2020, and estimated the selectivity and retention curves from the available data. Discard mortality was either estimated from the observer data (USA and Canada) or fixed at values taken from the literature (see SCRS/2022/124).

3.6 Stock-Recruitment

Expected recruitment to age-0 was calculated from the total spawning stock biomass using the Beverton-Holt stock-recruit function. The standard error for the log-normally distributed recruitment deviations (sigmaR) was fixed to 0.2. Steepness (h) was fixed at the value estimated in the 2017 assessment (0.88).

4.1 OM Uncertainty Grid

In 2019, the Swordfish Species Group developed an OM uncertainty grid using a full factorial design with 6 axes of uncertainty:

1. Natural mortality (M) - three levels: 0.1, 0.2, 0.3

2. Recruitment variability (sigmaR; \(\sigma_R\)) - two levels: 0.2, 0.6

3. Steepness (h) - three levels: 0.6, 0.75, 0.90

4. CPUE Lambda - three levels: 0.05, 1, 20

5. Directional trend in catchability (llq) - two levels: TRUE, FALSE

6. Environmental covariate (env) - two levels: TRUE, FALSE

The directional increase in catchability was modeled by assuming a 1% average annual increase in catchability throughout the history of the fishery, and adjusting the CPUE indices accordingly.

The environmental covariate was included by modeling catchability as a function of the AMO, as was done in the 2017 and 2022 stock assessments.

This factorial design of the 6 axes of uncertainty, each with 2 or 3 levels, resulted in an uncertainty grid of 216 OMs.

4.2 Evaluation of the OM Grid

Previous analyses of the OM uncertainty grid based on the 2017 assessment revealed that the three levels of natural mortality and steepness had the largest impact on the predicted stock dynamics Hordyk et al., 2021 and are therefore the most important axes of uncertainty in the OM grid.

The second level of recruitment variability (\(\sigma_R = 0.6\)) had a minor impact on the predicted stock dynamics Hordyk et al., 2021, but did influence the relative performance of candidate management procedures Hordyk, 2021. This second level is now treated as a robustness test (see below for more details).

The fourth axis of uncertainty was intended to evaluate the effect of alternative relative weightings of the length composition data and the indices of abundance. The three levels reflect a complete down-weighting of the indices of abundance (0.05; effectively only fitting the model to the length composition data), leaving the relative weighting of the two data sources unchanged from that used in the assessment (1), and up-weighting the indices of abundance so the model ignores the length composition data (20). This was done because of apparent conflicting signals between the length composition data and some of the indices of abundance, and the high computation demand of conducting the recommended iterative re-weighting procedure across all OMs in the grid (Francis, 2011). However, this iterative re-weighting procedure has now been conducted for the new operating models based on the 2022 assessment, and therefore this axis of uncertainty has been modified to two levels: 1) fit the assessment to both length and CPUE data and conduct the iterative re-weighting procedure, and 2) only fit the model to the CPUE data. This second level is now treated as a robustness test (see below for more details).

The fifth axis of uncertainty with the assumed 1% increase in catchability for the indices of abundance had a relatively minor influence on both the predicted stock dynamics Hordyk et al., 2021 and the performance of candidate management procedures Hordyk, 2021. Therefore it was decided that the default assumption for the operating models was to use the same indices as were used in the 2022 stock assessment; .e., the indices were not adjusted for an assumed 1% annual increase in changeability. The second level of this axis, where the indices are adjusted for an assumed increase in catchability, is now treated as a robustness test (see below for more details).

In both the 2017 and 2022 stock assessments, the catchability coefficient (q) for the CPUE indices for Canada, Japan, Portugal, Morocco, and the Spain age-specific survey indices, was made a function of the Atlantic Multidecadal Oscillation (AMO). Including this environmental covariate resulted in a better statistical fit to the data for these indices. The sixth axis of uncertainty examined the impact of not including this environmental covariate in the stock assessment. The analyses revealed that removing the environmental covariate from the assessment model had no detectable influence on either the predicted stock dynamics Hordyk et al., 2021 or the performance of candidate management procedures Hordyk, 2021. Therefore, the environmental covariate was included in all models in the OM grid. Further examination of the impact of changing environmental conditions on the performance of the candidate management procedures may be examined in additional robustness tests (see below for more details).

This same pattern in results was found when the analyses were re-conducted with the new OM grid based on the 2022 assessment.

5.1 Base Case

The Base Case operating model is the 2022 Stock Assessment. See the Stock Assessment section for more details.

5.2 Reference Operating Models

The Reference OMs are based on the Base Case Model and span the plausible range for the key uncertainties. The Candidate Management Procedures (CMPs) are tuned across the Reference OMs (see the CMP Tuning section for more details.

Based on the analyses described above, a set of 6 operating models were identified as the Reference OMs. These operating models spanned the three levels of M (0.1, 0.2, and 0.3) and two levels h (0.75 and 0.9).

Initially the OMs with the lower steepness value \((h=0.6)\) was also considered to be in the Reference OMs. However, recent analysis and discussion with the MSE Technical Team has revealed that a such a low steepness is unlikely for North Atlantic swordfish. Consequently, the OMs with \(h=0.6\) have been re-classified into a Robustness Set. Further levels of steepness may be considered in future analyses if the results indicate that this axis of uncertainty is important for the performance and selection of the CMPs.

The Reference OMs had the following assumptions:

1. Following the assessment, \(\sigma_R = 0.2\)
2. The Francis iterative re-weighting procedure was conducted on each operating model to find the appropriate weighting between the length compostion data and the indices of abundance
3. The standardized indices of abundance were used (i.e., no assumed 1% increase in catchability)

5.3 R1. Lower Steepness

Robustness set 1 (R1. Lower Steepness) evaluates the performance of the CMPs under conditions where the stock has lower than expected resilience. These 3 OMs have the same assumptions as the Base Case, except that steepness (h) is fixed to 0.6, and natural mortality has three levels: 0.1, 0.2, and 0.3.

5.4 R2. Higher sigmaR

This set of 6 operating models has the same structure and assumptions as the Reference Set, with the exception that the recruitment variability was assumed to higher \((\sigma_R = 0.6)\)

5.5 R3. Remove CAL

This set of 6 operating models has the same structure and assumptions as the Reference Set, with the exception that the model was only fit to the indices of abundance (i.e., fits to the length composition data were not included in the total likelihood).

5.6 R4. Increase q

This set of 6 operating models had the same structure and assumptions as the Reference Set, with the exception that the indices of abundance were modified to assume an average 1% increase in catchability over the historical period.

5.7 R5. Increasing q

This set of 6 operating models has the same assumptions as R4. Increase q, except that the 1% average annual increase in catchability is assumed to continue in the future. The implementation of this assumption is made in during the forward projections by passing a custom function to the CMPs which adjusts the simulated indices of abundance assuming a 1% average annual increase in catchability.

5.8 R6. Implementation Error

This set of 6 operating models has the same assumptions as the Reference OMs, except that there is a consistent non-reported 10% overage in the TAC in each year. This test is designed to replicate illegal, unreported, or unregulated (IUU) fishing where the actual removals are 10% higher than the TAC and reported catches.

5.9 R7. Climate Change - Recruitment

This set of OMs are based on the Reference OMs and includes several scenarios to evaluate the impact of climate change on the performance of the CMPs. These scenarios are designed to simulate the impact of climate change on the productivity of the stock, and are implemented in the recruitment deviations for the projection years.

Figure 5.1 shows the Base Case (no trend in recruitment deviations) and the three scenarios:

1. Decreasing Trend: a linear decrease in recruitment strength in imposed over the recruitment deviations that are generated for the projection years. The recruitment deviations are assumed to decrease by 20% at the end of the projection period.

2. Increasing Trend: a linear increase in recruitment strength in imposed over the recruitment deviations that are generated for the projection years. The recruitment deviations are assumed to increase by 20% at the end of the projection period.

3. Increased Variability: additional variability is added to the future recruitment by sampling between the upper and lower bounds shown in the plot.

Figure 5.1: The Base Case and 3 scenarios for persistent patterns in recruitment deviations in the projection years.

5.10 R8. Size Limit

This set of 6 operating models has the same assumptions as the Reference OMs with the exception that the size limit is modified in the projection period. This change is implemented in the CMPs and includes two scenarios:

1. Fixed 120 cm Size Limit: retention is assumed knife-edge at 120 cm for all fleets, with discard mortality applied to the fish that are captured and released below this size.

2. No Size Limit: the size limit is removed and all fish that are captured are retained and contribute to the TAC.

These two scenarios are compared to the Reference OMs, which are designed to replicate the current size regulations (see above for more details).

5.11 R9. Alternative Management Cycles

This set of 6 operating models has the same assumptions as the Reference OMs with the exception that the management cycle is modified from the default assumption of 3 years (see below) to a 4-year interval.

6.1 OM Summary Report

A Summary Report summarizes the diagnostic checks, the calculated biological reference points, and the estimated stock status relative to those reference points, across the Reference and Robustness operating models.

6.2 OM Diagnostic Reports

Individual diagnostic reports with objective function values and plots of model fits and patterns in residuals are available for each of the Reference and Robustness OMs (and the 2022 assessment) on the North Atlantic Swordfish MSE homepage.

7.1 Historical Data

The observed fishery data was imported into the MSE framework and made available to the CMPs. Three sources of data available to the CMPs: 1) catch data, 2) a primary index of abundance, and 3) additional individual indices.

7.1.1 Catch Data

The catch data used in the OM conditioning are made available in the simulated data that is provided to the CMPs (Figure 7.1).

Figure 7.1: The historical total catch and total allowable catch (TAC) for the North Atlantic swordfish.

7.1.2 Indices of Abundance

7.1.2.1 Primary Index

At the 2020 Swordfish MSE technical meeting (4 – 5 June 2020), the Group chose to use the Combined Index (Ortiz et al., 2017) as the primary index for the development of CMPs. This index was updated for the 2022 assessment. The Combined Index was imported into the SWOMSE framework and made available to the CMPs (Figure 7.2).

Figure 7.2: The Combined Index for the North Atlantic swordfish fishery.

7.1.2.2 Other Indices

The additional individual fishery-dependent and survey indices are also imported into the MSE framework and made available to the CMPs (Figure 7.3).

Figure 7.3: The additional individual fishery-dependent and survey indices for the North Atlantic swordfish fishery.

8.1 Simulation Specifications

The current simulation specifications for the MSE are:

1. Management interval: 3 (i.e., TAC updated every 3rd year)
2. Number of projection years: 33 (2021 – 2053)
3. Number of simulations per OM: \(100\)

8.2 Assumptions

8.3 Generation of Future Data

8.4 Management Interval

At the 2021 Intersessional meeting, the Group proposed a 3-year MP advice interval (see Table 5 in the Report of the 2021 ICCAT Intersessional Meeting of the Swordfish Species Group).

The operating models have been conditioned on data up to 2020. As described above, the catches in 2021 – 2023 are fixed to assumed values and the CMPs are first applied in 2024.

The 3-yr management interval is applied within the CMP code (see the CMP Development Guide for more details).

Robustness Set 9 (R9) evaluates the impact of alternative management intervals.

9.1 Summary of Performance Metrics

The performance metrics are calculated over all the simulations (100) in each operating model. For example, PGK_short is calculated as the proportion of the 100 simulations where the spawning biomass (\(SB\)) is above \(SB_{MSY}\) and fishing mortality (\(F\)) is below \(F_{MSY}\).

The performance metrics can also be calculated over several operating models, for example the entire Reference Set. This is done by first combining the MSE results for each OM (e.g., the 6 Reference OMs) into a single set of results (in this example, resulting in 600 simulations (6 OMs multiplied by 100 simulations per OM)), and then calculating the performance metrics over all results.

The Minimum Acceptable Performance values listed in Table 9.1 will be used as alternative values to determine if candidate management procedures meet the minimum performance criteria.

More details on the individual performance metrics are in the sections below.

9.2 Peformance Metrics Examples

The following sections show figures and statistics for examples from each family of Performance Metrics. These results include two example candidate management procedures (IR1_a, SP1_c) (see Candidate Management Procedures), with a single operating model that includes 10 stochastic simulations.

9.3 Status

9.3.1 PGK_10

PGK_10 calculates the probability of being in Green Zone of Kobe Space (SB>SBMSY & F<FMSY) in year 10 (2033). This is calculated as the proportion of the total number of simulations that are in the Green Zone of Kobe Space in 2033.

Figure 9.1 shows an example of two CMPs (IR1_a, SP1_c) for an operating model with 10 simulations. The values for PGK_10 in this example are: 0, 0.4.

Figure 9.1: An example of time-series plots of F/FMSY and SB/SBMSY for two CMPs. This OM has 10 simulations, shown as the colored lines. Simulations where SB>SBMSY and F<FMSY in 2033 are indicated with a green point, and those outside of the green space in the Kobe plot are indicated in red. The Kobe plot on the bottom shows these points in the Kobe space. The numbers in the corners indicate the percent of points in each area of the Kobe space.

9.3.2 PGK_med

PGK_med calculates the probability of being in Green Zone of Kobe Space (SB>SBMSY & F<FMSY) over years 6–10 (2029–2033). This is calculated as the proportion of the total points (number of simulations x number of years) that are in the Green Zone of Kobe Space.

Figure 9.2 shows an example of two CMPs (IR1_a, SP1_c) for an operating model with 10 simulations. The values for PGK_med in this example are: 0.06, 0.36.

Figure 9.2: An example of time-series plots of F/FMSY and SB/SBMSY for two CMPs. This OM has 10 simulations, shown as the colored lines. Simulations where SB>SBMSY and F<FMSY in years 2029–2033 are indicated with a green point, and those outside of the green space in the Kobe plot are indicated in red. The Kobe plot on the bottom shows these points in the Kobe space. The numbers in the corners indicate the percent of points in each area of the Kobe space.

9.3.3 PGK_long

PGK_long calculates the probability of being in Green Zone of Kobe Space (SB>SBMSY & F<FMSY) over years 11–30 (2034–2053). This is calculated as the proportion of the total points (number of simulations x number of years) that are in the Green Zone of Kobe Space.

Figure 9.3 shows an example of two CMPs (IR1_a, SP1_c) for an operating model with 10 simulations. The values for PGK_long in this example are: 0.24, 0.64.

Figure 9.3: An example of time-series plots of F/FMSY and SB/SBMSY for two CMPs. This OM has 10 simulations, shown as the colored lines. Simulations where SB>SBMSY and F<FMSY in years 2034–2053 are indicated with a green point, and those outside of the green space in the Kobe plot are indicated in red. The Kobe plot on the bottom shows these points in the Kobe space. The numbers in the corners indicate the percent of points in each area of the Kobe space.

9.3.4 PGK

PGK calculates the probability of being in Green Zone of Kobe Space (SB>SBMSY & F<FMSY) over all years (2024–2053). This is calculated as the proportion of the total points (number of simulations x number of years) that are in the Green Zone of Kobe Space.

Figure 9.4 shows an example of two CMPs (IR1_a, SP1_c) for an operating model with 10 simulations. The values for PGK in this example are: 0.18, 0.5.

Figure 9.4: An example of time-series plots of F/FMSY and SB/SBMSY for two CMPs. This OM has 10 simulations, shown as the colored lines. Simulations where SB>SBMSY and F<FMSY are indicated with a green point, and those outside of the green space in the Kobe plot are indicated in red. The Kobe plot on the bottom shows these points in the Kobe space. The numbers in the corners indicate the percent of points in each area of the Kobe space.

9.3.5 PGK_30

PGK_30 calculates the probability of being in Green Zone of Kobe Space (SB>SBMSY & F<FMSY) in year (2053). This is calculated as the proportion of the total number of simulations that are in the Green Zone of Kobe Space.

Figure 9.5 shows an example of two CMPs (IR1_a, SP1_c) for an operating model with 10 simulations. The values for PGK_30 in this example are: 0.6, 0.8.

Figure 9.5: An example of time-series plots of F/FMSY and SB/SBMSY for two CMPs. This OM has 10 simulations, shown as the colored lines. Simulations where SB>SBMSY and F<FMSY in 2053 are indicated with a green point, and those outside of the green space in the Kobe plot are indicated in red. The Kobe plot on the bottom shows these points in the Kobe space. The numbers in the corners indicate the percent of points in each area of the Kobe space.

9.3.6 POF and PNOF

POF calculates the probability of over-fishing (F>FMSY) over all years (2024–2053). This is calculated as the proportion of the total number of points (number of simulations x number of years) where F is greater than FMSY.

Figure 9.6 shows an example of two CMPs (IR1_a, SP1_c) for an operating model with 10 simulations. The values for POF in this example are: 0.62, 0.29.

PNOF reports the probability of not overfishing, calculated as \(1-\text{POF}\), i.e. 0.38, 0.71.

Figure 9.6: An example of a time-series plot of F/FMSY for a two CMPs. This OM has 10 simulations, shown as the colored lines. Simulations where F<FMSY are indicated with a green point, and red points indicate F>FMSY. The Kobe plot on the bottom shows these points in the Kobe space. The numbers in the corners indicate the percent of points in each area of the Kobe space.

9.4 Safety

9.4.1 LRP_short and nLRP_short

LRP_short calculates the probability of breaching the limit reference point (LRP; SB<0.4SBMSY) in any of the first 10 years (2024-2033).

This is calculated as the proportion of simulations where SB<0.4SBMSY in any of the first 10 years.

Figure 9.7 shows an example of two CMPs (IR1_a, SP1_c) for an operating model with 10 simulations. The values for LRP_short in this example are: 0, 0.

nLRP_short reports the probability of not breaching the LRP, calculated as \(1-\text{LRP_short}\), i.e. 1, 1.

Figure 9.7: An example of a time-series plot of SB/SBMSY for a two CMPs. This OM has 10 simulations, shown as the colored lines. Simulations where SB>0.4SBMSY are indicated with a green point, and red points indicate SB<0.4SBMSY. The Kobe plot on the bottom shows these points in the Kobe space. The numbers in the corners indicate the percent of points in each area of the Kobe space.

9.4.2 LRP_long and nLRP_long

LRP_long calculates the probability of breaching the limit reference point (LRP; SB<0.4SBMSY) in any of years 11-30 (2034-2053).

This is calculated as the proportion of simulations where SB<0.4SBMSY in any of the last 20 years.

Figure 9.8 shows an example of two CMPs (IR1_a, SP1_c) for an operating model with 10 simulations. The value for LRP_long in this example are: 0, 0.

nLRP_long reports the probability of not breaching the LRP, calculated as \(1-\text{LRP_long}\), i.e. 1, 1.

Figure 9.8: An example of a time-series plot of SB/SBMSY for two CMPs. This OM has 10 simulations, shown as the colored lines. Simulations where SB>0.4SBMSY are indicated with a green point, and red points indicate SB<0.4SBMSY. The Kobe plot on the bottom shows these points in the Kobe space. The numbers in the corners indicate the percent of points in each area of the Kobe space.

9.4.3 LRP and nLRP

LRP calculates the probability of breaching the limit reference point (LRP; SB<0.4SBMSY) in any of the projection years (2024-2053).

This is calculated as the proportion of simulations where SB<0.4SBMSY in any year.

Figure 9.9 shows an example of two CMPs (IR1_a, SP1_c) for an operating model with 10 simulations. The value for LRP in this example are: 0, 0.

nLRP reports the probability of not breaching the LRP, calculated as \(1-\text{LRP}\), i.e. 1, 1.

Figure 9.9: An example of a time-series plot of SB/SBMSY for a two CMPs. This OM has 10 simulations, shown as the colored lines. Simulations where SB>0.4SBMSY are indicated with a green point, and red points indicate SB<0.4SBMSY. The Kobe plot on the bottom shows these points in the Kobe space. The numbers in the corners indicate the percent of points in each area of the Kobe space.

9.5 Yield

9.5.1 C1

C1 calculates the median TAC (t) in the first year (2024). This is calculated as the median across the simulations of the 2024 TAC (in most cases the TAC will be the same for all simulations in the first implementation year).

Figure 9.10 shows an example of two CMPs (IR1_a, SP1_c) for an operating model with 10 simulations. The values for C1 in this example are: 13913, 15745.

Figure 9.10: An example of time-series plots of the TAC for two CMPs. This OM has 10 simulations, shown as the colored lines. The points indicate the values used to calculate the median value.

9.5.2 AvC10

AvC10 calculates the median landed catch (t) in the first 10 years (2024–2033). This is calculated as the median catch across the simulations and years.

Figure 9.11 shows an example of two CMPs (IR1_a, SP1_c) for an operating model with 10 simulations. The values for AvC10 in this example are: 12768, 12507.

Figure 9.11: An example of time-series plots of the TAC for a two CMPs. This OM has 10 simulations, shown as the colored lines. The points indicate the values used to calculate the median value.

9.5.3 AvC30

AvC30 calculates the median landed catch (t) in the last 20 years (2034–2053). This is calculated as the median catch across the simulations and years.

Figure 9.12 shows an example of two CMPs (IR1_a, SP1_c) for an operating model with 10 simulations. The values for AvC30 in this example are: 9933, 6331.

Figure 9.12: An example of time-series plots of the TAC for two CMPs. This OM has 10 simulations, shown as the colored lines. The points indicate the values used to calculate the median value.

9.6 Stability

9.6.1 VarC and MaxVarC

VarC calculates the median absolute change in the TAC between management cycles. MaxVarC reports the maximum change in TAC across all years and simulations.

These values are calculated as the absolute relative change in the TAC from one management cycle to the next.

Figure 9.13 shows an example of two CMPs (IR1_a, SP1_c) for an operating model with 10 simulations. The values for VarC in this example are: 0.06, 0.25. The value for MaxVarC in this example are: 0.2, 0.25.

Figure 9.13: An example of time-series plots of the relative change in the TAC between management cycles for two CMPs. This OM has 10 simulations, shown as the colored lines. The median and maximum absolute change are shown in the top left corner.

10.1 Tuning of Candidate Management Procedures

During development, the Candidate Management Procedures are tuned to achieve specific performance outcomes. These performance outcomes are calculated across all the Reference OMs; i.e, the MSE results from each Reference OM are aggregated together. 8 different tuning targets have been specified (Table 10.1).

There will be 8 versions of each CMP corresponding to metrics and targets described in Table 10.1. The names of the CMPs include the tuning code (e.g., ‘a’, ‘b’, etc) as a subscript at the end.

10.2 Overview of Candidate Management Procedures

For tunings a – d, all MPs are subject to a constraint on the maximum absolute change in TAC between management cycles (Table 10.1).

10.3 Index Ratio (IR)

10.4 Surplus Production (SP)

11.1 Summary Tables

11.2 Summary Plots

11.3 Interactive Application

TODO - describe Slick